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Abstract:

Data mining concepts are used frequently throughout the transportation research
sector. This paper uses the concept of the market basket technique on public
transport users as a means of gaining more insight into their transport demands.
The paper proposes a method that uses various data attributes of passenger records
to infer the same customer in a different week i.e attempts to track the same
customer from week to week. The general idea behind the measure is that, if two
records are considered similar, ideally every trip in one customer record should have
a close counterpart in the other record. The research develops a similarity function
and this aims to maximise the percentage of positive ticket identification over a
number of weeks. Once similarity has been established, the travel patterns of
customers can be useful in helping the operator identify new routes, new timetables
and strategic decisions in relation to satisfying public transport customer demands.